Pre-sale and second-hand housing transaction modes dominate China’s real estate market. However, many existing studies tend to treat the housing market as a homogeneous entity, overlooking the heterogeneity in core i...Pre-sale and second-hand housing transaction modes dominate China’s real estate market. However, many existing studies tend to treat the housing market as a homogeneous entity, overlooking the heterogeneity in core influencing factors across different transaction types. Thoroughly understanding the factors affecting various housing types can assist policymakers in formulating differentiated regulatory decisions through environmental intervention. Therefore, this study utilized multi-source big data and compared the performance of multiple machine learning models to evaluate the relative importance and nonlinear effects of buildinglevel, neighborhood-level, and street-level built environment factors on pre-sale and secondhand housing prices. The empirical study of Chengdu, China revealed that distance to city center was the most significant explanatory factor influencing pre-sale and second-hand housing prices among all factors. Significant differences existed between neighborhood-level and street-level built environment factors’ nonlinear and threshold effects on pre-sale and second-hand housing prices. Notably, subway accessibility showed a U-shaped impact on pre-sale housing prices. To the best of our knowledge, our study is one of the early studies systematically investigating the influencing differences between pre-sale housing prices and second-hand housing prices, providing robust evidence for regulating housing prices through environmental interventions and offering critical references for policymakers and market participants.展开更多
Streets play a crucial role in the pedestrian catchment area(PCA)of metro stations.However,the large-scale quality measurement of street space and its influence on the vitality of station area have not been well revea...Streets play a crucial role in the pedestrian catchment area(PCA)of metro stations.However,the large-scale quality measurement of street space and its influence on the vitality of station area have not been well revealed.With multisource big data such as points of interest(POI),and street view images,a three-dimensional evaluation system based on the pyramid scene parsing network(PSPNet)and spatial design network analysis(sDNA)is constructed.73 metro stations in the Third Ring Road of Chengdu are chosen as research samples to carry out large-scale quantitative evaluation of street space in PCAs to reveal the quality characteristics of street space at the overall urban,PCA,and circle scales.Furthermore,this study constructs two multiple linear regression models of weekdays and weekends to explore the relationship between urban vitality and street space quality indicators.The results indicate a heterogeneous distribution of street quality on an urban scale.Streets located in the 300-500 m of PCAs rate highest in terms of convenience and the overall street space quality.The functionality dimension of street spaces in the sample PCAs of Chengdu present a gradient effect with the highest score of 0-300 m in the circle,while the comfortability dimension of streets shows an opposite trend.The multiple linear regression analysis show that street quality indicators are more explanatory of the weekday vitality than the weekend vitality.It indicates that well-connected street network,pleasant street scale,and abundant urban facilities have the greatest effect on urban vitality in the PCAs.The findings can provide new ideas for making targeted interventions in the urban design of metro station areas,to improve the quality of streets and foster urban vitality.展开更多
基金supported by the 2024 Zhejiang Provincial Philosophy and Social Sciences Planning“Provincial and municipal cooperation”Project(No.24SSHZ140YB).-。
文摘Pre-sale and second-hand housing transaction modes dominate China’s real estate market. However, many existing studies tend to treat the housing market as a homogeneous entity, overlooking the heterogeneity in core influencing factors across different transaction types. Thoroughly understanding the factors affecting various housing types can assist policymakers in formulating differentiated regulatory decisions through environmental intervention. Therefore, this study utilized multi-source big data and compared the performance of multiple machine learning models to evaluate the relative importance and nonlinear effects of buildinglevel, neighborhood-level, and street-level built environment factors on pre-sale and secondhand housing prices. The empirical study of Chengdu, China revealed that distance to city center was the most significant explanatory factor influencing pre-sale and second-hand housing prices among all factors. Significant differences existed between neighborhood-level and street-level built environment factors’ nonlinear and threshold effects on pre-sale and second-hand housing prices. Notably, subway accessibility showed a U-shaped impact on pre-sale housing prices. To the best of our knowledge, our study is one of the early studies systematically investigating the influencing differences between pre-sale housing prices and second-hand housing prices, providing robust evidence for regulating housing prices through environmental interventions and offering critical references for policymakers and market participants.
基金funded by China Postdoctoral Science Foundation(Grant No.2020M673222)the Natural Science Foundation of Sichuan Province(Grant No.2023NSFSC0898)the National Natural Science Foundation of China(Grant No.52108021).
文摘Streets play a crucial role in the pedestrian catchment area(PCA)of metro stations.However,the large-scale quality measurement of street space and its influence on the vitality of station area have not been well revealed.With multisource big data such as points of interest(POI),and street view images,a three-dimensional evaluation system based on the pyramid scene parsing network(PSPNet)and spatial design network analysis(sDNA)is constructed.73 metro stations in the Third Ring Road of Chengdu are chosen as research samples to carry out large-scale quantitative evaluation of street space in PCAs to reveal the quality characteristics of street space at the overall urban,PCA,and circle scales.Furthermore,this study constructs two multiple linear regression models of weekdays and weekends to explore the relationship between urban vitality and street space quality indicators.The results indicate a heterogeneous distribution of street quality on an urban scale.Streets located in the 300-500 m of PCAs rate highest in terms of convenience and the overall street space quality.The functionality dimension of street spaces in the sample PCAs of Chengdu present a gradient effect with the highest score of 0-300 m in the circle,while the comfortability dimension of streets shows an opposite trend.The multiple linear regression analysis show that street quality indicators are more explanatory of the weekday vitality than the weekend vitality.It indicates that well-connected street network,pleasant street scale,and abundant urban facilities have the greatest effect on urban vitality in the PCAs.The findings can provide new ideas for making targeted interventions in the urban design of metro station areas,to improve the quality of streets and foster urban vitality.
